• Journal of Korean Society for Atmospheric Environment
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• v.19 no.6
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• pp.625-637
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• 2003

This study was performed to examine the relationship between air pollution exposure and mortality in Seoul for the years of 1998∼2001. Daily counts of death were analyzed by general additive Poisson model, with adjustment for effects of seasonal trend, air temperature, humidity, and day of the week as confounders in a nonparametric approach. Daily death counts were associated with CO (current day),O$_3$ (current day), PM$_{10}$ (current day), NO$_2$ (1 day before), SO$_2$ (1 day before). Increase of 41.71 $\mu\textrm{g}$/㎥ (interquartile range) in PM$_{10}$ was associated with 1.3% (95% CI = 0.7∼1.9％) increase in the daily number of death. $O_3$ concentrations resulted in an increased risk of 1.3% for 23.86 ppb in all-aged mortality [RR = 1.013 (1.004-1.023)1. This effect was greater in children (less than 15 aged) and elderly (more than 65 aged). After ozone level exceeds 25 ppb, the dose-response relationship between mortality and ozone was almost linear. We concluded that Seoul had 1∼5％ increase in mortality in association with IQR (interquartile range) in air pollutants. Daily variations in air pollution within the range currently occurring in Seoul might have an adverse effect on daily mortality. These findings also support the hypothesis that air pollution, at levels below the current ambient air quality standards of Korea, is harmful to sensitive subjects, such as children or elderly.rly.

• Health Policy and Management
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• v.30 no.4
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• pp.493-504
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• 2020

Background: There have been many studies on the associations between body mass index (BMI) and cognitive function. However, no study has ever compared the associations across the methods of categorizing BMI. In this study, we aimed to fill the gap in the previous studies and examine whether the obesity paradox is valid in the risk of cognitive function. Methods: Of the 10,254 people aged 45 and older from the Korean Longitudinal Study of Ageing from 2006 to 2016, 8,970 people were finalized as the study population. The dependent variable was whether a person has a normal cognitive function or not, and the independent variables of interest were BMI categorized by the World Health Organization Western Pacific Regional Office (WHO-WPRO) method, the WHO method, and a 10-group method. Covariates included sociodemographic factors, health behavior factors, and health status factors. A generalized linear mixed model analysis with a logit link was used. Results: In the adjusted model with all covariates, first, in the case of BMI categories of the WHO-WPRO method, underweight (odds ratio [OR], 1.16; 95% confidence interval [CI], 1.15-1.17), overweight (OR, 1.36; 95% CI, 1.35-1.36), and obese (OR, 1.34; 95% CI, 1.33-1.34) groups were more likely to have a normal cognitive function than a normal-weight group. Next, in the case of BMI categories of the WHO method, compared to a normal-weight group, underweight (OR, 1.15; 95% CI, 1.14-1.16) and overweight (OR, 1.06; 95% CI, 1.06-1.07) groups were more likely to have a normal cognitive function; however, obese (OR, 0.62; 95% CI, 0.61-0.63) group was less likely to have it. Lastly, in the case of the 10-group method, as BMI increased, the likelihood to have a normal cognitive function changed like a wave, reaching a global top at group-7 (26.5 kg/㎡ ≤ BMI <28.0 kg/㎡). Conclusion: The associations between BMI and cognitive function differed according to how BMI was categorized among people aged 45 and older in Korea, which suggests that cognitive function may be positively associated with BMI in some categories of BMI but negatively in its other categories. Health policies to reduce cognitive impairment need to consider this association between BMI and cognitive function.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• The Journal of the Korea Contents Association
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• v.18 no.10
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• pp.165-174
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• 2018

The purpose of this study was to examine the association metabolic syndrome with sarcopenia in Korean stroke patients. We used the Korean National Health and Nutrition Examination Survey data from 2008 to 2011 and enrolled a total of 316 stroke patients older than 40 years. Data were analyzed by Rao-Scott ${\chi}^2-test$, generalized linear model and composite sample multiple logistic regression. The prevalence of sarcopenia was 47.3% in men and 46.3% in women. The prevalence of metabolic syndrome was 50.3% in men and 73.6% in women. After adjusting for age, smoking status, alcohol consumption, exercise, education, income, sroke sequla and stroke duration, men with sarcopenia had increased risk of metabolic syndrome (95% CI: 2.454-18.482, p<.001). This finding can be used to develop evidence-based health promotion program to prevent stroke reccurance for stroke patients.

• Journal of Korean Society of Transportation
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• v.18 no.6
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• pp.89-99
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• 2000

To cope with the limits of conventional O-D trip matrix collecting methods, several approaches have been developed. One of them is bilevel Programming method Proposed by Yang(1995), which uses Sensitivity Analysis Based(SAB) algorithm to solve Generalized Least Square(GLS) problem. However, the SAB a1gorithm has revealed two critical short-comings. The first is that when there exists a significant difference between target O-D matrix and true O-D matrix, SAB algorithm may not produce correct solution. This stems from the heavy dependance on the historical O-D information, in special when gravel Patterns are dramatically changed. The second is the assumption of iterative linear approximation to original Problem. Because of the approximation, SAB algorithm has difficulty in converging to Perfect Stackelberg game condition. So as to avoid the Problems. we need a more robust and stable solution method. The main purpose of this Paper is to show the problem of the dependency of Previous models and to Propose an alternative solution method to handle it. The Problem of O-D matrix estimation is intrinsically nonlinear and nonconvex. thus it has multiple solutions. Therefore it is necessary to require a method for searching globa1 solution. In this paper, we develop a solution algorithm combined with genetic algorithm(GA) , which is widely used as probabilistic global searching method To compare the efficiency of the algorithm, SAB algorithm suggested by Yang et al. (1992,1995) is used. From the results of numerical example, the Proposed algorithm is superior to SAB algorithm irrespective of travel patterns.

• Korean Journal of Ichthyology
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• v.20 no.2
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• pp.105-111
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• 2008

A pelagic tuna longline research cruise in the eastern and central Pacific Ocean from September to October of 2006 was conducted to compare catch rates with the use of different hook type and bait combinations. Traditional tuna hooks (J 4) and three circle hook types (C15, C16, C18), along with five bait types (chub mackerel (CM), jack mackerel (JM), milkfish (MF), sardine (SD), and squid (SQ)) and hook number as a proxy for hook depth were evaluated for their effect on bigeye tuna catch rates (fish per 1,000 hooks) using Generalized Linear Models (GLMs). Results from 28 sets indicated significant differences in bigeye catch rates between individual longline sets and hook number. The GLM explained 33% of the deviance in bigeye catch rates with these two factors. An alternative model formulation included bait type which had a small effect (explaining 2.7% of the deviance) on catch rates. Hook type had a negligible and non-significant effect in the GLMs. These results indicate that all of the hooks and baits tested are equally effective at catching bigeye tuna and that hook number (depth) was the paramount operational factor in explaining bigeye tuna catch rates.

• Korean Journal of Community Nutrition
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• v.20 no.2
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• pp.129-140
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• 2015

Objectives: We examined the association between the adherence to dietary guidelines for breast cancer survivors and health-related quality of life in a cross-sectional study of Korean breast cancer survivors. Methods: A total of 157 women aged 21 to 79 years who had been diagnosed with stage I to III breast cancers according to the American Joint Committee on Cancer (AJCC) and had breast cancer surgery at least 6 months before the baseline were included. We used a Korean version of the Core 30 (C30) and Breast cancer 23 (BR23) module of the European Organization for Research and Treatment Cancer Quality of Life Questionnaire (EORTC-QLQ), both of which have been validated for Koreans. Participants were asked about their adherence to dietary guidelines for breast cancer survivors, suggested by the Korean breast cancer society, using a 5-point Likert scale. We summed dietary guideline adherence scores for each participant and calculated the least squares means of health-related quality of life according to dietary guideline adherence scores using the generalized linear model. Results: Breast cancer survivors who had higher adherence to dietary guidelines for breast cancer survivors had lower constipation scores than those with lower adherence (p for trend=0.01). When we stratified by the stage at diagnosis, this association was limited to those who had been diagnosed with stage II or III breast cancers. Also, sexual functioning scores increased significantly with increasing adherence scores of dietary guidelines among those with stage II or III breast cancers (p for trend < 0.001). However, among those who had been diagnosed with stage I, higher scores of dietary guidelines were associated with higher scores of pain (p for trend=0.03) and breast symptoms (p for trend=0.05). Conclusions: Our study suggested that the health-related quality of life levels of breast cancer survivors are associated with the adherence to dietary guidelines and may differ by the stage of the breast cancer.

• Korean Journal of Community Nutrition
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• v.19 no.2
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• pp.163-175
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• 2014

Objectives: We aimed to evaluate dietary intake among female breast cancer survivors in a cross-sectional study. Methods: A total of 127 women who had breast cancer surgery at least 6 months before baseline were included. Dietary intake of female breast cancer survivors was assessed through self-reported 3 day-dietary records. To compare dietary intake between breast cancer survivors and general female population without cancer, we selected the 1:4 age matched women from the 2011 Korean National Health and Nutrition Examination Survey (KNHANES). In the KNHANES, participants were asked about their dietary intake using the 24-hour dietary recalls. We also examined whether dietary intake varied by age group, cancer stage, or time since surgery among breast cancer survivors. We used the generalized linear model to compare their dietary intakes. Results: Intakes of total energy, beta-carotene, folate, vitamin C, plant iron and fruits were lower among breast cancer survivors with longer time since surgery compared to those with shorter time (p<0.05). Breast cancer patients with higher stage at diagnosis tended to consume less legumes (p=0.01) than those with lower stage. When we compared dietary intake between breast cancer survivors and the general female population without cancer, breast cancer survivors were more likely to consume most of macro- and micro-nutrients in larger quantity (p<0.05) and adhere to healthier diet characterized by higher intakes of legumes, seed and nuts, vegetables and fishes and shells than the general female population who never had been diagnosed with cancer (p<0.05). Conclusions: Our study results suggested that the intakes of nutrients and foods varied by time since surgery and cancer stage among breast cancer survivors and dietary intakes among breast cancer survivors differed from that in the general population. Further prospective studies are warranted to explore the association between dietary intakes of specific food items and survival among Korean breast cancer survivors.

• Korean Journal of Environmental Biology
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• v.35 no.4
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• pp.640-647
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• 2017

The need for an efficient sampling technique to collect aquatic organisms has risen with the increase of interest in rice paddy fields, which have been recognized as important ecosystems supporting biodiversity. In the present study, a submerged funnel trap used with the assistance of attracting agents (fish meal and chemical light) was designed as an easy, objective and quantitative tool for collecting aquatic organisms in the rice paddy fields. The preference for collecting aquatic organisms as a means for attracting agents was analyzed using a generalized linear mixed model. Also, based on the data of previous research, we compared the community composition of the aquatic macroinvertebrates, which were collected using the quadrat method, and newly designed submerged funnel traps, by analyzing non-metric multidimensional scaling. The results showed that the catching efficiency of 18 of the total 65 taxa was affected by the attracting agents. 12 taxa including Pomacea canaliculata, Hippeutis cantori, Austropeplea ollula, Erpobdella lineata, Ostracoda spp. Branchinella kugenumaensis, Hydaticus grammicus, Rhantus pulverosus, Chironomidae spp., Rana nigromaculata, Cobitidae spp. etc., favored fish meal and 6 taxa including Ischnura asiatica, Coenagrionidae spp. Sternolophus rufipes etc., were attracted by chemical light. The submerged funnel trap used as a measurement tool for biodiversity was less applicable than the quadrat method; however, it was more effective for the selective collection of specific taxa. We expect that this newly designed trap can be a simple and quantitative method for collecting aquatic organisms, and could be used for long term and extensive surveys in rice paddy fields in the future.